A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

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“…Another recent MI-based speller, the Oct-O-Spell was introduced in [ 40 ]. The GUI is similar to the Hex-O-Spell.…”

Section: Review Of Bci Spellersmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

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“…Further, individualizing the system can improve classification performance by as much as 18%, as we showed by increasing the stimulus duration, which recommends adaptive interfaces. Adaptive interfaces might maximize efficiency using general-purpose templates, as shown by our cross-classification analysis, or introduce new features such as predictive text [54][55][56][57][58][59] or mental imagery decoding [60] to narrow the search space of possible user intentions, increasing efficiency especially for low SNR users.…”

Section: Discussionmentioning

confidence: 99%

An open interface system for non-invasive brain-to-brain free communication between naive human participants

Renton

Mattingley

2018

Preprint

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Keywordsamyotrophic lateral sclerosis, anarthria, BCI speller, brain-machine interface, computer keyboards, electroencephalography (EEG), filter-bank canonical correlation analysis (CCA), hyperscanning, open science, neuroimaging, quadriplegia, steady-state visual evoked potential (SSVEP), virtual reality (VR), visual selective attention AbstractFree and open communication is fundamental to modern life. Brain-computer interfaces (BCIs), which translate measurements of the user's brain activity into computer commands, present emerging forms of hands-free communication. BCI communication systems have long been used in clinical settings for patients with paralysis and other motor disorders, and yet have not been implemented for free communication between healthy, BCI-naïve users.Here, in two studies, we developed and validated a high-performance non-invasive BCI communication system, and examined its feasibility for communication during free word association and unprompted free conversation. Our system, focusing on usability for free communication, produced information transfer rates sufficient and practical for free association and brain-to-brain conversation (~5.7 words/minute). Our findings suggest that performance appraisals for BCI systems should incorporate the free communication scenarios for which they are ultimately intended. To facilitate free and open communication in healthy users and patients, we have made our source code and data open access. OverviewFree and open communication is fundamental to modern life, scientific enterprise and democratic discourse. The rising prevalence of technologies such as virtual/augmented reality [1][2][3][4] and artificial intelligence [5; 6] has created new opportunities for hands-free communication and control. Brain-computer interfaces (BCI), which translate measurements of the user's brain activity into computer commands to control external devices [7][8][9][10][11] , present emerging forms of hands-free communication. BCI spellers are virtual keyboards that decode brain activity patterns allowing users to select characters in sequence to spell words and, ultimately, freely communicate [12] . BCI keyboards mimic manual keyboards, which extend the user by allowing them to physically manifest their real-time thoughts, interface with the internet and communicate remotely. BCI communication systems, including spellers, have long been used in clinical settings to facilitate communication in cases of quadriplegia, anarthria and amyotrophic lateral sclerosis [13][14][15] . These systems are often developed using electroencephalography (EEG), which allows for portable, flexible and affordable devices [16] . BCI has the potential to revolutionise communication, and yet its potential for creating free communication in healthy users is largely unexplored [17] . Here we introduce a new and efficient non-invasive system, using sparse-electrode electroencephalography (EEG), that allows free communication between individuals based on real-time brain activity decoding.Remarkable pro...

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“…The support vector machine (SVM) and other neural network algorithms are employed for BCI classification. () However, the low signal‐noise ratio (SNR) of EEG signals is disadvantageous for classification . Therefore, the precisions of MI‐based BCI was lower than 80% in previous studies.…”

Section: Introductionmentioning

confidence: 95%

Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition

Wang

Cao

Zhang

et al. 2018

Concurrency and Computation

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Summary Motor imagery (MI) is an important control paradigm in the field of brain‐computer interface (BCI), which enables the recognition of personal intention. So far, numerous methods have been designed to classify EEG signal features for MI task. However, deep neural networks have been seldom applied to analyze EEG signals. In this study, two novel kinds of deep learning schemes based on convolutional neural networks (CNN) and Long Short‐Term Memory (LSTM) were proposed for MI‐classification. The frequency domain representations of EEG signals were obtained using short time Fourier transform (STFT) to train models. Classification results were compared between conventional algorithm, CNN, and LSTM models. Compared with two other methods, CNN algorithms had shown better performance. These conclusions verified that CNN method was promising for MI‐based BCIs.

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A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller

Cited by 36 publications

References 47 publications

Brain–Computer Interface Spellers: A Review

Brain–Computer Interface Spellers: A Review

An open interface system for non-invasive brain-to-brain free communication between naive human participants

Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition

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